The volatility impact of social expenditure’s cyclicality in advanced economies

Journal Pre-proof The volatility impact of social expenditure’s cyclicality in advanced economies João Tovar Jalles

PII: DOI: Reference:

S0313-5926(19)30148-1 https://doi.org/10.1016/j.eap.2020.02.002 EAP 355

To appear in:

Economic Analysis and Policy

Received date : 24 April 2019 Revised date : 3 February 2020 Accepted date : 4 February 2020 Please cite this article as: J.T. Jalles, The volatility impact of social expenditure’s cyclicality in advanced economies. Economic Analysis and Policy (2020), doi: https://doi.org/10.1016/j.eap.2020.02.002. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2020 Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.

Journal Pre-proof

repro of

The volatility impact of social expenditure’s cyclicality in advanced economies * João Tovar Jalles# December 2019 Abstract

rna lP

We present a new dataset of time-varying measures of social spending cyclicality in a sample of 26 advanced countries between 1982 and 2012. More specifically, we focus on five categories of government social expenditure: health, social protection, pensions, education and welfare. Results show that health and education spending is generally acyclical, while pensions are procyclical and social protection and welfare spending are counter-cyclical. That said, sample averages hide serious heterogeneity across countries. Our findings suggest that the higher the degree of countercyclicality of government’s social spending, the lower output volatility will be. Results are robust to several specifications, the use of alternative dependent variables, and estimators (including those accounting for endogeneity).

*

Jou

Keywords: education, health, pensions, time-varying coefficients, panel data, instrumental variables, institutions JEL codes: C22, C23, H50, H60, H62

The author thanks comments and suggestions for an anonymous referee. Thanks also go to Davide Furceri and Xavier Debrun for extensive discussions on the topic. The usual disclaimer applies. Any remaining errors are the author’s sole responsibility. The opinions expressed herein are those of the author and do not reflect those of his employer. # UECE – Research Unit on Complexity and Economics. Rua Miguel Lupi 20, 1249-078 Lisbon, Portugal. UECE is financially supported by FCT (Fundação para a Ciência e a Tecnologia), Portugal. This article is part of the Strategic Project (UID/ECO/00436/2019). Economics for Policy and Centre for Globalization and Governance, Nova School of Business and Economics, Rua da Holanda 1, 2775-405 Carcavelos, Portugal. Email: [email protected]

Journal Pre-proof

repro of

The volatility impact of social expenditure’s cyclicality in advanced economies January 2020 Abstract

rna lP

We present a new dataset of time-varying measures of social spending cyclicality in a sample of 26 advanced countries between 1982 and 2012. More specifically, we focus on five categories of government social expenditure: health, social protection, pensions, education and welfare. Results show that health and education spending is generally acyclical, while pensions are procyclical and social protection and welfare spending are counter-cyclical. That said, sample averages hide serious heterogeneity across countries. Our findings suggest that the higher the degree of countercyclicality of government’s social spending, the lower output volatility will be. Results are robust to several specifications, the use of alternative dependent variables, and estimators (including those accounting for endogeneity).

Jou

Keywords: education, health, pensions, time-varying coefficients, panel data, instrumental variables, institutions JEL codes: C22, C23, H50, H60, H62

Journal Pre-proof

1. Introduction

repro of

From a policy point of view, it is important to understand how government expenditure behaves around the economic business cycle. Expenditure patterns may change as a result of policy makers’ discretionary actions or due to the (free) operation of automatic stabilizers (Granado et al., 2013). Government’s social spending policy in particular has a stabilizing effect on the economy if one of its categories (e.g. spending on social protection or health) rises when output growth declines and vice-versa (Furceri, 2010). This is a desirable feature of fiscal policy from a stabilization perspective and a characteristic present in most advanced economies (Talvi and Vegh, 2005; Staehr, 2008; Egert, 2012).1

Empirical papers assessing the cyclical properties of government expenditure can be divided into three: i) those that document the cyclical properties of fiscal policy and its components (Hallerberg and Strauch, 2002 for advanced countries and finding evidence of acyclical or countercyclical government expenditure; Gavin et al., 1996; Kaminsky et al, 2005; Alesina et al., 2008 for developing economies and finding evidence of a procyclical behaviour); ii) those that

rna lP

inspect their determinants2; and iii) those that assess their growth effects (Lee and Chang, 2006; Furceri and Zdzienicka, 2012; D’Addio, 2015). Few have looked at how the degree of cyclicality of social spending affects macroeconomic volatility (see e.g. Darby and Melitz, 2008; Furceri, 2010 and Ovaska and Palardy, 2014) which is a gap we aim to fill with this work.3 Since output volatility negatively affects medium-term growth through its effects on investment and productivity, fiscal policy - through its social dimension - can foster medium-term growth by reducing aggregate macroeconomic volatility (Ramey and Ramey, 1995).4 The existing empirical evidence linking fiscal (counter-)cyclicality and growth is mixed. That said, most seem to agree

1

Jou

that a timely countercyclical response of fiscal policy to (demand) shocks will deliver lower output Discussions on the cyclicality patterns of fiscal policy are generally centred around two main theories: the Keynesian approach and the Neoclassical tax-smoothing model (Barro, 1979). The Keynesians posit that governments should spend and tax countercyclically (Prasad and Gerecke, 2010). In contrast, Barro’s tax-smoothing model recommends acyclical fiscal policy. 2 Several explanations have been developed to justify the different cyclical patterns in different income groups: i) inadequate access to international credit markets and lack of financial depth (Gavin and Perotti, 1997; Calderon and Schmidt-Hebbel, 2008); ii) political distortions and weak institutions (Tornell and Lane, 1999; Alesina et al., 2008; Talvi and Vegh, 2005; Acemoglu et al. 2013; and Fatas and Mihov 2013). 3 The cyclicality of social spending was thoroughly analysed by Darby and Melitz (2008), which, estimating fiscal reactions functions for different social spending categories found that several of them acted as automatic stabilizers. 4 The idea that fiscal policy can affect productivity growth by operating in a counter-cyclical way has been suggested by Aghion et al. (2005). This prediction finds also empirical support in firm-level based studies (Berman et al. 2007). 2

Journal Pre-proof

and consumption volatility (Van den Noord 2000; Kumhof and Laxton 2009; Debrun and Kapoor 2011; Fatas and Mihov 2012).

repro of

This paper has two key research questions which it tries to answer empirically. First, how stabilizing is government’s social policy in advanced countries and how has cyclicality evolves through time, across different countries and around turning points? Second, what is the relationship between social spending (pro-)cyclicality and aggregate macroeconomic volatility? We answer these questions employing a new empirical approach by estimating time-varying measures of different categories of social spending cyclicality. We focus on a sample of 26 advanced countries between 1980 and 2012.5 To the best of our knowledge, this is the first paper that estimates timevarying measures of different categories of social spending cyclicality. Moreover, we evaluate how does the degree of social spending cyclicality impact aggregate macroeconomic volatility in a panel setting. The use of time-varying measures overcomes the major limitation of other studies that relied on cross-country regressions and were not able to account for country-specific as well as global factors.

The main results can be summarized as follows. Health and education spending are found

rna lP

to be acyclical, while pensions exhibit a procyclical behaviour and social protection and welfare spending are counter-cyclical. We uncover that sample averages hide serious heterogeneity across countries. We then rely on panel data regression analysis to find that health spending procyclicality increases output volatility (measured by the absolute value of a new measure of output gap computed using the recent Hamilton (2018) filter). Similar results are obtained in the case of education spending cyclicality. Social protection and welfare spending cyclicality do not seem do affect output volatility. An increase in the degree of pension spending procyclicality reduces aggregate macroeconomic volatility. Results are robust to a number sensitivity checks. The paper is structured as follows. Section 2 outlines the methodology and discusses the

Jou

data. Section 3 presents key stylized facts. Section 4 discusses the empirical results. Section 5 concludes.

5

The selection of countries was based on the criteria of having at least 20 continuous time-series observations for a given social spending category to be able to properly estimate a time-varying coefficients model. 3

Journal Pre-proof

2. Methodology and Data

repro of

2.1 Time-Varying Social Spending Cyclicality Social spending has an economic stabilizing effect if one of its categories increases when output growth declines and vice-versa (Furceri, 2010). The more countercyclical government social spending is, the higher its stabilizing effect. In contrast, government social spending is destabilizing when it is procyclical.

We begin by assessing the degree of social spending cyclicality in each country i by estimating the response of changes in a given social spending category to changes in economic activity. Mathematically, we run the following reduced-form specification6: ∆ln 𝑠𝑠

𝛽 ∆ln 𝑦

𝛼

𝜀

(1)

where ∆ is a first-difference operator; 𝑠𝑠 is a social spending category (expressed in real terms using the GDP deflator) in country i at time t (in years), 𝑦 proxies economic activity and it is in country i: 𝛽

rna lP

represented by real GDP. The key coefficient 𝛽 measures the degree of social spending cyclicality 0 corresponds to social spending procyclicality; 𝛽

0 corresponds to social

spending counter-cyclicality. Five social spending categories are considered: health, education, pensions, social protection , and welfare. Expenditure on health refers to public spending on health care such as publicly financed investment in health facilities plus capital transfers to the private sector for hospital construction and equipment for instance. Education expenditure denotes current, capital and transfer spending on education and it includes all on budget pre-primary, primary, secondary and tertiary education, as well as any adult learning programmes. Pension expenditure corresponds to social security and similar statutory programmes administered by the general

Jou

government (including other retirement benefits etc.). Social protection includes contributory social insurance transfers (e.g. unemployment benefits) and social assistance benefits (e.g. family benefits, unemployment assistance). Welfare services includes special programmes for the elderly, orphans or disabled, needs-based transfers, food stamps, non-contributory pensions. Data for these variables, as well as for real GDP and its deflator are taken from the IMF World Economic Outlook and Government Finance Statistics databases. 6

Several papers have employed this first difference specification – Lane (1998), Thornton (2008) and Woo (2009) . 4

Journal Pre-proof

Then, we generalize equation (1) by introducing the assumption that coefficients vary over time: 𝛼

𝛽 ∆ln 𝑦

𝜀

repro of

∆ln 𝑠𝑠

(2)

𝛽 is now assumed to change slowly and unsystematically over time and its conditional expected value today is equal to yesterday’s value. The change of the coefficient 𝛽 is denoted by 𝑣 , , which is assumed to be normally distributed with expectation zero and variance 𝜎 7: 𝛽

𝛽

𝑣

(3)

Equation (2) and (3) are jointly estimated using the Varying-Coefficient model proposed by Schlicht (1985). Variances 𝜎 are calculated by a method-of-moments estimator that coincides with the maximum-likelihood estimator for large samples (Schlicht, 1985; Schlicht, 2003; Schlicht and Ludsteck, 2006).8

rna lP

This approach has several advantages compared to other methods to compute time-varying coefficients (Aghion and Marinescu, 2008). First, it allows using all observations in the sample to estimate the degree of social spending cyclicality in each year—which by construction is not possible in the rolling windows approach. Second, changes in the degree of social spending cyclicality in a given year come from innovations in the same year, rather than from shocks occurring in neighbouring years. Third, it reflects the fact that changes in policy are slows and depends on the immediate past. Fourth, it reduces reverse causality problems when social spending cyclicality is used as explanatory variable as it depends on its own past.

Jou

2.2 Effects of Social Spending Cyclicality

Next, we evaluate the effect of social spending cyclicality on output volatility. To this end, the following reduced-form specification is estimated based on a panel of 26 advanced economies for which we have estimates of social spending cyclicality for at least 20 continuous years: 7

Table A1 in the Appendix shows that this assumption is satisfied. The approach proposed by Schlicht (2003) is very similar to that used by Aghion and Marinescu (2008). The main difference is in the computation of the variances 𝜎 . Aghion and Marinescu (2008) use the Markov Chain Monte Carlo (MCMC) method to approximate these variances, while Schlicht (2003) uses a method-of-moments estimator. 8

5

Journal Pre-proof

𝛿

𝛾

𝜗𝛽

𝝅′𝒁𝒊𝒕

𝜖

(4)

repro of

𝑣𝑜𝑙

where 𝛽 is the measure of social spending cyclicality estimated earlier for country i at time t (note that higher values of this variable are associated to more procyclical social spending and for interpretation purposes we expect a positive sign for 𝜗 as the premise is that more procyclicality fuels macroeconomic volatility); 𝛿 are country-fixed effects to capture unobserved heterogeneity and time-unvarying factors; 𝛾 are time-fixed effects to control for global shocks. 𝑣𝑜𝑙 denotes output volatility—measured by the absolute value of output gap— in country i at time t. We use as baseline the absolute deviation of output gap to maximize the number of observations in our sample. Despite substantial progress in the estimation methodologies to calculate potential output, there is still not a widely accepted approach in the profession. Researchers typically adopt two alternative methods to estimate potential GDP (Borio, 2013): i) univariate statistical approaches, which usually consist of filtering out the trend component from

rna lP

the cyclical one; ii) structural approaches, which derive the estimates directly from the theoretical structure of a model. Aware of their shortcomings9, we rather apply the recent filter proposed by Hamilton (2018). We do so also mindful of the criticisms surrounding the popular use of the Hodrick-Prescott (HP) filter (such as the identification of spurious cycles, inter alia) in the context of a large heterogeneous sample (see Harvey and Jaeger, 1993; Cogley and Nason, 1995). Hamilton’s (2018) approach to extract the cyclical and trend component of a generic variable 𝑥 (denoted 𝑥

and 𝑥 , respectively), consists of estimating the following regression:

𝑥

∑

where 𝑥 𝑥

𝑢

𝑥

𝛾

𝑥

𝑢

(5)

𝑥 . The non-stationary part of the regression provides the cyclical component:

Jou

𝛾

(6)

while the trend is given by

9

Statistical methods suffer from the end-point problem, that is, they are extremely sensitive to the addition of new data and to real-time data revisions. Structural models, on the other hand, may be difficult to implement consistently in cross-sectional environments and rely on the imposition of pre-determined assumptions. 6

Journal Pre-proof

𝑥

∑

𝛾

𝛾

𝑥

(7)

Hamilton (2018) suggests that h and k should be chosen such that the residuals from equation

repro of

(5) are stationary and points out that, for a broad array of processes, the fourth differences of a series are indeed stationary. We choose h = 2 and k = 3, which is line with the dynamics seen in real GDP.

For robustness purposes, we also check alternative volatility measures such as the standard deviation of either the output gap or real GDP growth. To reduce potential endogeneity problems due to omitted variables that may simultaneously affect output volatility and social spending cyclicality, we include a set of lagged controls(𝒁𝒊𝒕 ), namely: (i) trade openness; (ii) capital account openness; (iii) credit-to-GDP ratio; (iv) GDP per capita; (v) GDP growth; (vi) population; and (vii) government size.10These controls have been found in the literature to be associated with macroeconomic volatility. Countries more open to trade and with less capital account restrictions tend to be more exposed to external shocks and heightened volatility (Rodrik, 1998; Lane, 2003).

rna lP

More developed countries (those with higher GDP per capita and growth rates) tend to be better insulated to fluctuations also as a result for better institutions and sounder structural factors (Talvi and Vegh, 2005). In addition, as discussed in Fatas and Mihov, (2013) and Debrun and Kapoor (2011), the larger the government, the more likely is the economy to be cushioned against negative shocks due to the operation of automatic stabilizers.

Equation (4) is estimated by Ordinary Least Squares (OLS) with robust standard errors clustered at the country level.

Jou

3. Stylized Facts

We first report the average level and the time path of the coefficient of social spending cyclicality estimated in equation (2) and (3) for a panel of at most 26 advanced countries for which we have time-varying estimates for at least 20 continuous years (Figure 1). Depending on the social

10

Table A2 in the Appendix presents data definitions and sources while Table A3 displays key summary statistics of all variables used. 7

Journal Pre-proof

spending category in question, the number and composition of countries may change due to data availability.11

repro of

As a first observation, it is worth noting that the time-average health spending cyclicality coefficient is positive (about 0.3), which is consistent with the fact that this type of expenditures in our sample is generally procyclical. However, based on the one standard deviation confidence bands we cannot reject the null that the response of changes in real health spending to changes real GDP is zero (that is, we get, generally, acyclicality). On the one hand, health spending may increase during downturns (in a counter-cyclical way) since firms may either fire or give incentives for workers to retire earlier (in both cases if there is a health plan associated with the labor contract, it will cease to exist); on the other, health spending may also increase during good times since as the pace of work is speedier, there could be more work-related accidents, particularly in dangerous dynamic industries. The net effect is thus ambiguous as we can see in the summary chart. For social protection spending cyclicality, the time-average coefficient equals -0.4, with fluctuations between -1.2 and 0.4. The coefficient has becoming more negative (and increasing its statistical significance) over time hinting to some counter-cyclical behaviour (despite some reduction in its

rna lP

degree following the Global Financial Crisis). A similar pattern can be observed for welfare spending cyclicality. It has generally been negative and slightly decreasing over time. Typically, active labor market policies and family (and other disadvantaged groups) support-related spending are used by governments to sustain employment and household incomes during downturns. Pensions, in contrast, are all and everywhere strongly procyclical and the degree of procyclicality has been on the rise in the early 2000s. This is not surprising since pensions are often connected with the growth of wages.12 Finally, regarding the cyclicality of education spending, while in the early 1980s it was clearly (and significantly) procyclical, over time we saw this spending

11

Jou

category’s degree of procyclicality reduced to a point when it is acyclical.

For health expenditures we have a sample comprising of 26 countries; for social production expenditures we cover 26 countries; for pension expenditures we have 24 countries; for education expenditures we use data for 18 countries; and, finally, for welfare expenditures we use data for 24 countries. 12 We thank an anonymous referee for this point. 8

Journal Pre-proof

Figure 1. Social Spending Cyclicality Over Time Health

Social protection 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4

repro of

1.2

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 TVC coefficient over time

AVG TVC coefficient

AVG + 1 S.D.

AVG - 1 S.D.

TVC+ 1 S.D.

TVC - 1 S.D.

Pensions

TVC coefficient over time

AVG TVC coefficient

AVG + 1 S.D.

AVG - 1 S.D.

TVC+ 1 S.D.

TVC - 1 S.D.

Education

1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6

2

1.5

1

0.5

0

-0.5 -1

rna lP

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

TVC coefficient over time

AVG TVC coefficient

AVG + 1 S.D.

AVG - 1 S.D.

TVC coefficient over time

AVG TVC coefficient

TVC - 1 S.D.

AVG + 1 S.D.

AVG - 1 S.D.

TVC+ 1 S.D.

TVC - 1 S.D.

TVC+ 1 S.D.

Welfare 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 -3.5

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 AVG TVC coefficient

Jou

TVC coefficient over time AVG + 1 S.D.

AVG - 1 S.D.

TVC+ 1 S.D.

TVC - 1 S.D.

Note: the figure displays the time profile of the time-varying coefficient estimates for four different social spending categories and covering countries with at least 20 observations. Confidence bands are shown for both the time-average and time-varying estimates based on plus or minus one standard deviation.

The second observation concerns the country heterogeneity hidden by the average time profile previously discussed. Figure 2 plots the average time-varying cyclicality for the five different social spending categories. Indeed, there is great variation with two relatively clear pictures: the

9

Journal Pre-proof

fact that all countries show procyclical pension spending and most shows countercyclical welfare and social protection expenditure. Concerning the other two categories, some countries present a

repro of

procyclical behaviour others procyclical and yet others acyclical. Such heterogeneous picture just uncovered justifies the use of a panel data regression in the second stage with country fixed effects. Figure 2. Average Social Spending Cyclicality by country Cyclicality of Health Expenditures

Cyclicality of Social Protection Expenditures

2

1

0.5

1.5

‐0.5

0.5

‐1

‐0.5 ‐1

Belgium United States United Kingdom Netherlands Japan Canada Denmark Ireland Switzerland New Zealand Finland Singapore Greece Israel France Hong Kong SAR Norway Sweden Korea Spain Australia Germany Italy Iceland Austria Portugal

0

Cyclicality of Pension Expenditures

‐1.5

Singapore Switzerland Korea Canada France United States Norway Spain United Kingdom Netherlands Ireland Finland Belgium Australia Sweden Japan New Zealand Denmark Greece Italy Austria Germany Hong Kong SAR Iceland Portugal Israel

0

1

‐2

‐2.5

Cyclicality of Education Expenditures

3.5

2

3 2

rna lP

1.5

2.5

1

1.5 1

0

Israel

Greece

Canada

Norway

Finland

Sweden

Iceland

Australia

Denmark

Germany

Netherlands

New Zealand

Korea

Portugal

Switzerland

United Kingdom

Belgium

United States

Italy

Japan

Austria

Spain

Ireland

0

France

0.5

0.5

‐0.5

‐1

‐12

Austria

Iceland

Israel

Portugal

Denmark

Ireland

Greece

Japan

Belgium

Sweden

Spain

Finland

Italy

France

Canada

Australia

Korea

United Kingdom

‐8 ‐10

Netherlands

‐6

Norway

‐4

Switzerland

0 ‐2

United States

2

Jou

4

New Zealand

6

Germany

Cyclicality of Welfare Expenditures 8

Country specific charts for each social spending category displaying time-varying coefficient estimates are available in Figure A1 in the Appendix. The degree of health spending procyclicality 10

Journal Pre-proof

has increased (decreased) over time for 3 (15) out of 26 countries in the sample. Some, after a period of increasing procyclicality, saw an inversion of the previous trend (e.g. Japan, Austria or

repro of

Israel). Concerning social protection spending counter-cyclicality, several of the set of advanced economies covered saw an increase in its degree over time (e.g. Australia, Austria, Iceland, Italy, Japan, Portugal, US). In others the degree of counter-cyclicality has stabilized in recent years (e.g. Finland, Ireland). In the case of pensions spending cyclicality, several countries experienced a decline in the degree of its procyclicality since the early to mid-1990s (e.g. Belgium, Sweden, UK). In contrast, countries like Israel, Canada, Japan or Portugal, the rise in procyclicality over time has been the norm. When it comes to education spending cyclicality, we also have a more homogeneous picture: most countries have seen their degree of procyclicality declining over time. Finally, regarding welfare spending, we see again quite some heterogeneity with some countries increasing its counter-cyclicality degree over time (e.g. US, Greece, Switzerland) while others experienced the opposite (e.g. Netherlands, Norway, Canada).

Figure 3. Social Spending Cyclicality during Recessions Health

Social protection

rna lP

Health Cyclicality Before, During and After  Recessions

Social Protection Cyclicality Before, During and  After Recessions

0.0000

0.4000

‐0.0500

0.3500

t‐2

t‐1

‐0.2500

0.1500

‐0.3000

0.1000

‐0.3500 ‐0.4000

0.0500

‐0.4500

0.0000 t‐2

t‐1

t

t+1

t+2

‐0.5000

Pensions

Pensions Cyclicality Before, During and After  Recessions 1.1100

0.4000

1.0750 1.0700 1.0650

0.3500

Jou

1.1000

1.0800

Education Education Cyclicality Before, During and After  Recessions

0.4500

1.1050

1.0850

t+2

‐0.2000

0.2000

1.0900

t+1

‐0.1500

0.2500

1.0950

t

‐0.1000

0.3000

t‐2

t‐1

t

0.3000 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000

t+1

t+2

t‐2

Welfare

11

t‐1

t

t+1

t+2

Journal Pre-proof

Education Cyclicality Before, During and After  Recessions ‐1.1200 ‐1.1400

t‐2

t‐1

t

t+1

t+2

‐1.1600

‐1.2000 ‐1.2200 ‐1.2400 ‐1.2600 ‐1.2800 ‐1.3000 ‐1.3200

repro of

‐1.1800

Note: the figure displays the average value of the time-varying coefficient estimates from 2 years prior to the beginning of a given recession (“t”) to two years after it began.

The third observation is that, interestingly, while pensions and education spending procyclicality seems to have increased during recessions (defined as years of negative real GDP growth), in contrast, the degree of counter-cyclicality of both social protection and welfare expenditures has increased during recessions. There is no clear pattern regarding health around such turning points (Figure 3).

4.1 Baseline results

rna lP

4. Empirical Findings

We start with a parsimonious specification of equation (4), using only country- and timefixed effects as control variables. The results reported in specification of Table 1.1-1.5 for each of the social spending categories. Looking at Table 1.1 first, the coefficient on health spending (pro)cyclicality suggests that health spending pro-cyclicality increases output volatility. In particular, results suggest that an increase of 0.4 in our measure of health spending cyclicality (about 2 standard deviations) increases output volatility by about 0.1 percentage points. Similar results are obtained in the case of education spending cyclicality (Table 1.4). Social protection spending

Jou

cyclicality does not seem do affect output volatility (resulting coefficient estimates are not statistically different from zero) – Table 1.2. Welfare spending procyclicality come out with positive but seldomly statistically significant coefficients (Table 1.5). Interestingly, an increase in the degree of pension spending procyclicality seems to lower aggregate macroeconomic volatility: an increase of 1.2 in our measure of pension spending cyclicality (about 2 standard deviations) decreases output volatility by about 0.8 percentage points. In order to limit reverse causality, we re-estimated the baseline specification using the lags of each spending cyclicality measure instead. 12

Journal Pre-proof

The results reported in specifications 2 of each Table (1.1-1.5) are generally similar to the contemporaneous alternative.

repro of

Results are generally robust when the controls variables discussed above are included (specifications 3 and 4). Among the control variables, we find that credit-to-GDP is positively associated with output volatility; while larger countries (given by population size) tend to be characterized by lower output volatility (this result is consistent with Furceri and Karras, 2007). Table 1.1 The effect of health spending cyclicality on output volatility Specification Regressors

(1)

Health spending (pro)cyclicality (t)

0.192* (0.118)

Health spending (pro)cyclicality (t-1)

Capital account openness (t-1) Credit to GDP (t-1)

Log population (t-1)

Government expenditure to GDP (t-1) Country f.e. Time f.e. Observations R-squared

Yes Yes 798 0.329

Yes Yes 800 0.317

(4)

(5)

(6)

0.198 (0.149)

0.272** 0.148 (0.127) (0.134) -1.105** -1.090** -0.806* -0.825* (0.487) (0.500) (0.496) (0.504) 0.067 0.051 -0.072 -0.068 (0.084) (0.087) (0.101) (0.102) 0.328*** 0.217 0.888*** 0.979*** (0.123) (0.139) (0.224) (0.229) 1.137* 1.181* -0.988 -0.883 (0.620) (0.659) (0.706) (0.733) -0.058* -0.056* (0.032) (0.034) -7.680*** -9.171*** (2.183) (2.247) -0.126** -0.134** (0.050) (0.052) Yes Yes Yes Yes Yes Yes Yes Yes 699 678 626 611 0.366 0.361 0.368 0.379

rna lP

GDP per capita (t-1)

(3)

0.209* (0.125)

0.346*** (0.118)

Trade openness (t-1)

GDP growth (t-1)

(2)

Jou

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively.

13

Journal Pre-proof

Table 1.2 The effect of social protection spending cyclicality on output volatility Specification Regressors

(1) 0.057 (0.120)

Social protection spending (pro)cyclicality (t-1) Trade openness (t-1) Capital account openness (t-1) Credit to GDP (t-1) GDP per capita (t-1) GDP growth (t-1) Log population (t-1) Government expenditure to GDP (t-1) Country f.e. Time f.e. Observations R-squared

(3)

(4)

0.004 (0.127)

(5)

(6)

0.199 (0.187)

repro of

Social protection spending (pro)cyclicality (t)

(2)

0.029 (0.119)

Yes Yes 750 0.333

Yes Yes 752 0.323

-0.033 0.003 (0.126) (0.131) -1.090** -1.146** -0.765 -0.853* (0.495) (0.502) (0.504) (0.506) 0.090 0.076 -0.060 -0.053 (0.085) (0.086) (0.102) (0.102) 0.293** 0.172 1.038*** 1.027*** (0.124) (0.138) (0.225) (0.226) 1.215* 1.238* -1.267* -0.948 (0.632) (0.666) (0.714) (0.737) -0.050 -0.049 (0.033) (0.034) -8.050*** -8.666*** (2.260) (2.252) -0.112** -0.125** (0.052) (0.052) Yes Yes Yes Yes Yes Yes Yes Yes 682 676 610 609 0.365 0.356 0.373 0.377

rna lP

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively. Table 1.3 The effect of pension spending cyclicality on output volatility Specification Regressors

Pension spending (pro)cyclicality (t)

(1)

-0.677*** (0.189)

Pension spending (pro)cyclicality (t-1) Trade openness (t-1)

(2)

GDP per capita (t-1)

Jou

GDP growth (t-1)

-0.700*** (0.221)

-0.683*** (0.192)

Capital account openness (t-1) Credit to GDP (t-1)

(3)

0.404 (0.814) 0.102 (0.083) 0.157 (0.124) 0.771 (0.890)

Log population (t-1)

Government expenditure to GDP (t-1) Country f.e. Time f.e. Observations R-squared

Yes Yes 510 0.389

Yes Yes 512 0.367

Yes Yes 477 0.412

(4)

(5)

(6)

-0.505** (0.226) -0.669*** (0.232) 0.481 0.615 (0.839) (0.849) 0.091 0.024 (0.086) (0.102) 0.026 0.965*** (0.139) (0.216) 0.540 -1.880** (0.927) (0.955) -0.078** (0.034) -3.921* (2.242) -0.097* (0.052) Yes Yes Yes Yes 463 458 0.399 0.390

-0.469** (0.237) 0.547 (0.868) 0.018 (0.103) 1.037*** (0.223) -1.807* (0.975) -0.079** (0.035) -5.597** (2.313) -0.108** (0.054) Yes Yes 445 0.396

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively. 14

Journal Pre-proof

Table 1.4 The effect of education spending cyclicality on output volatility (1)

Education spending (pro)cyclicality (t) Education spending (pro)cyclicality (t-1) Trade openness (t-1) Capital account openness (t-1) Credit to GDP (t-1) GDP per capita (t-1) GDP growth (t-1) Log population (t-1) Government expenditure to GDP (t-1) Country f.e. Time f.e. Observations R-squared

(2)

0.259** (0.125)

(3)

(4)

0.258** (0.131)

(5)

(6)

0.276** (0.126)

repro of

Specification Regressors

0.268** (0.125)

Yes Yes 510 0.389

Yes Yes 512 0.367

0.218* 0.262** (0.135) (0.128) -1.185** -1.110** -0.690 -0.649 (0.540) (0.551) (0.548) (0.561) -0.031 -0.059 -0.146 -0.169 (0.108) (0.113) (0.121) (0.125) 0.515*** 0.365** 1.106*** 1.275*** (0.144) (0.165) (0.307) (0.313) 0.867 0.802 -2.098** -2.214** (0.698) (0.750) (0.850) (0.882) -0.032 -0.025 (0.038) (0.040) -2.973 -3.384 (2.825) (2.954) -0.090 -0.109* (0.064) (0.065) Yes Yes Yes Yes Yes Yes Yes Yes 477 463 458 445 0.412 0.399 0.390 0.396

rna lP

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively. Table 1.5 The effect of welfare spending cyclicality on output volatility Specification Regressors

Welfare spending (pro)cyclicality (t)

(1)

(2)

0.110 (0.074)

Welfare spending (pro)cyclicality (t-1)

0.115 (0.080)

0.118* (0.073)

Trade openness (t-1)

Capital account openness (t-1) Credit to GDP (t-1)

GDP per capita (t-1)

Jou

(3)

1.224 (0.797) 0.056 (0.084) 0.187 (0.127) 0.091 (0.902)

GDP growth (t-1)

Log population (t-1)

Government expenditure to GDP (t-1) Country f.e. Time f.e. Observations R-squared

Yes Yes 693 0.322

Yes Yes 694 0.316

15

Yes Yes 645 0.340

(4)

(5)

(6)

0.224** (0.100) 0.112 0.131 (0.079) (0.085) 1.235 1.222 1.114 (0.802) (0.826) (0.830) 0.052 -0.060 -0.041 (0.085) (0.100) (0.100) 0.065 1.019*** 1.003*** (0.140) (0.222) (0.224) -0.118 -2.528*** -2.212** (0.931) (0.958) (0.975) -0.071** -0.070** (0.034) (0.035) -6.505*** -7.024*** (2.293) (2.294) -0.036 -0.062 (0.055) (0.054) Yes Yes Yes Yes Yes Yes 640 574 573 0.331 0.328 0.327

Journal Pre-proof

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively.

repro of

Some of the variables such as trade openness, GDP per capita and government size—which are typically found to be associated with output volatility in cross-countries studies (see e.g. Fatas and Mihov 2001; Debrun and Kapoor 2011)—are also statistically significant in our case but not always (depends on the spending category and set of controls under consideration). The reason for this relates with the inclusion of country-fixed effects which purge most of their variability. Indeed, they come out highly more significant when equation (4) is re-estimated by excluding country fixed effects (results available upon request).

To account for the possibility that the relation between social spending cyclicality and output volatility has changed over time, we extend equation (4) by interacting each cyclicality measure with dummies for pre- and post-2000s, respectively: 𝛿

𝛾

𝜗 𝐷

𝛽

𝜗 𝐷

𝛽

𝝅′𝒁𝒊𝒕

𝜖

rna lP

𝑣𝑜𝑙

(8)

Table 2 shows the results obtained from estimating equation (8) (only 𝜗 and 𝜗 are shown for reasons of parsimony and because the coefficient estimates on the vector 𝒁𝒊𝒕 are in line with those in tables 1.1-1.5). As one can see, the effect of social spending procyclicality on output volatility has changes over time but the depending on the social category under scrutiny effects are different. While the (positive) impact of procyclicality on volatility decreased over time for health, social protection and pensions, the reverse happened in education spending. No statistically significant difference between the two periods can be found regarding the impact of welfare spending cyclicality on volatility. These results are consistent with the dynamics in the cyclicality

Jou

coefficients of each social spending category observed in many countries (Figure A1).13

13

Similar results are obtained if we split the time sample into two equal periods (available upon request). 16

Journal Pre-proof

Table 2. The effect of spending cyclicality on output volatility, across time Specification Selected regressor \ social spending

(1) health

(2) social protection -0.112 (0.262) 0.330* (0.202) Yes Yes 610 0.376

(4) (5) education welfare

-0.855*** 1.173*** 0.258** (0.279) (0.280) (0.104) 0.042 0.124 0.208** (0.342) (0.131) (0.101) Yes Yes Yes Yes Yes Yes 589 458 574 0.326 0.408 0.330

repro of

Spending (pro)cyclicality (t) * Post 2000 -0.420* (0.228) Spending (pro)cyclicality (t) * Pre 2000 0.354** (0.154) Country f.e. Yes Time f.e. Yes Observations 626 R-squared 0.382

(3) pensions

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively.

4.2 Robustness checks

To check the robustness of our results we re-estimated equation (4) using alternative measures of output volatility: (i) the standard deviation of the output gap from the IMF WEO computed over a five-year rolling window; (ii) the standard deviation of real GDP growth computed on a five-year rolling window.14 Results presented in Table 3, confirm that the health decrease it.

rna lP

and education pro-cyclicality increase output volatility, while that stemming from pensions

Table 3. The effect of spending cyclicality on output volatility, alternative measures Specification Selected regressor \ dependent variable

(1)

Health spending (pro)cyclicality (t)

0.225** (0.117)

Social Protection spending (pro)cyclicality (t)

(2) (3) (4) S.D. of WEO output gap

(5)

(6)

-0.030

(0.103)

Jou

Yes Yes 626 0.170

-0.151* (0.085)

0.186* (0.105)

Welfare spending (pro)cyclicality (t) Country f.e. Time f.e. Observations R-squared

(0.069)

-0.192* (0.103)

Education spending (pro)cyclicality (t)

Yes Yes 610 0.193

Yes Yes 589 0.156

Yes Yes 458 0.288

(10)

0.330*** (0.855)

0.022

Pensions spending (pro)cyclicality (t)

(7) (8) (9) S.D. of real GDP growth

0.143* (0.078) 0.126** (0.055) Yes Yes 574 0.159

Yes Yes 626 0.052

Yes Yes 610 0.075

Yes Yes 589 0.020

Yes Yes 458 0.158

0.057 (0.037) Yes Yes 574 0.019

Note: Output volatility measures are the five-year rolling standard deviation of either the output gap (from WEO) or the real GDP growth rate (as identified in row 2). Results obtained by estimating equation (4). Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. ***,**,* denote significance at 1,5,10 percent level, respectively. 14

The use of the standard deviation computed on a five-year rolling window in the yearly dataset yields errors that are serially correlated within countries. We control for this possible bias by clustering the errors at the country level. 17

Journal Pre-proof

Given that our measures of cyclicality are based on estimates, we further check the

repro of

robustness of our results by re-estimating equation (4) with Weighted Least Squares, giving more weights to observations for which the degree of cyclicality is estimated more precisely (weights correspond to the inverse of the estimated standard errors associated with each 𝛽 ). This procedure yields larger effects on output volatility than before (specifications 1-5, Table 4).

Finally, a concern estimating equation (4) using OLS is that the results may be subject to reverse causality since governments concerned with output volatility could arguably adjust their fiscal behaviors to provide more stabilization. While in principle this issue is likely to not be relevant in our case, as our measures of social spending cyclicality depend on their own past, we still check the robustness of our results using an Instrumental Variable (IV) approach. Following Acemoglu et al. (2003) and Fatas and Mihov (2001, 2013), we select instruments capturing institutional and political characteristics of the countries likely to be correlated to our measures of social spending cyclicality but presumably not directly related to output volatility. The first instrument - labelled “constraints”, captures potential veto points on the decisions of the

rna lP

executive.15 A variation of this measure of constraints – and our second instrument - is a variable constructed by Henisz (2000) called “political constraints” (labelled “polcon”). This variable differs from the first measure in two ways: (1) the author adjusts for the ideological alignment across political institutions; and (2) he argues that each additional constraint has a diminishing marginal effect on policy outcomes and therefore the link between the overall measure and the veto points should be nonlinear. Another instrument considered is the lags of the cyclicality measure itself for each spending category. Results are presented in Table 4 where for the IV only the “constraints” instrument is employed; using alternatively the “polcon” yields qualitatively

Jou

similar results which are omitted for reasons of parsimony. To check the validity of our instruments and assess the strength of our identification, we rely on the Kleibergen-Paap and Hansen statistics.

15

The role of veto players in policymaking has been studied extensively in the political economy literature (Tsebelis, 2002). 18

Journal Pre-proof

Table 4. The effect of spending cyclicality on output volatility, alternative estimators (1)

Health spending (pro)cyclicality (t)

(2)

(4)

(5)

0.312* (0.182)

Social Protection spending (pro)cyclicality (t)

(6)

(7)

(8) IV

(0.737) Pensions spending (pro)cyclicality (t) Education spending (pro)cyclicality (t)

(0.272)

-0.475** (0.234)

0.714* (0.386)

Welfare spending (pro)cyclicality (t)

Kleibergen-Paap statistic (p-value) Hansen statistic (p-value)

(10)

0.159

-0.511** (0.257)

Yes Yes 626 0.425

(9)

0.311** (0.155) 0.031

Country f.e. Time f.e. Observations R-squared

(3) WLS

repro of

Specification Selected regressor \ estimator

Yes Yes 562 0.441

Yes Yes 570 0.366

Yes Yes 458 0.480

0.181 (0.127)

0.189 (0.217) Yes Yes 555 0.492

Yes Yes 597 0.404

Yes Yes 581 0.410

Yes Yes 574 0.331

Yes Yes 431 0.434

0.191* (0.111) Yes Yes 559 0.338

0.040 0.685

0.003 0.726

0.003 0.893

0.005 0.996

0.003 0.787

Note: Output volatility measured as the absolute value of the output gap. Results obtained by estimating equation (4). IV= lagged cyclicality measure and political constraints as instruments. Robust standard errors in parentheses clustered at the country level. Country fixed and time effects estimated but omitted for reasons of parsimony. The null hypothesis of the Kleibergen-Paap test is that the structural equation is underidentified (i.e., the rank condition fails) and tests that the excluded instruments are "relevant". Stock-Yogo critical values were applied. The Hansen test is a test of overidentifying restrictions. ***,**,* denote significance at 1,5,10 percent level, respectively.

Results reported in specifications 6-10 of Table 4 confirm that procyclicality increases

rna lP

output volatility in the cases of health and welfare spending, with the effects being slightly higher than the ones obtained with OLS. Robustly, an increase in the degree of procyclicality of pension spending lowers output fluctuations. In addition, looking at the diagnostic statistics to assess the validity of the instrumental variable strategy, the underidentification test p-values generally reject the null that the different equations are underidentified. Also, the Hansen test statistics reveal that the instrument sets contain valid instruments (i.e., uncorrelated with the error term, and that the excluded instruments are correctly excluded from the estimated equation).

Jou

5. Conclusion

Fiscal policy can influence medium-term growth through its support to macroeconomic stability. This paper explored the issue of social expenditure cyclicality by focusing on a panel of 26 advanced countries between 1980 and 2012.Using time-varying estimates of social spending cyclicality, we first provided a novel characterization of its behaviour across countries and over time and then we went on to empirically evaluate its effects on aggregate volatility.

19

Journal Pre-proof

In the first part of the paper we found that health and education spending are acyclical, while pensions are procyclical and social protection and welfare spending are counter-cyclical. The

repro of

degree of pro or counter-cyclicality has changed over time for some social spending categories (e.g. education went from a procyclical to an acyclical stance; social protection went from an acyclical to a counter-cyclical stance).Also, there exists a high degree of between-country heterogeneity hidden by the average time profiles that should not be overlooked.

In the second part, we relied on panel data estimations (from the most parsimonious to the most complex) relating social spending cyclicality to output volatility measured by the absolute value of a new measure of output gap computed using the recent Hamilton (2018) filter. We found that health spending pro-cyclicality increases output volatility: an increase of 0.4 in our measure of health spending cyclicality (about 2 standard deviations) increases output volatility by about 0.1 percentage points. Similar results were obtained in the case of education spending cyclicality. Social protection spending cyclicality did not seem do affect output volatility while welfare spending procyclicality came out with positive but seldomly statistically significant coefficients. Interestingly, an increase in the degree of pension spending procyclicality lowered aggregate

rna lP

volatility: an increase of 1.2 in our measure of pension spending cyclicality (about 2 standard deviations) decreased output volatility by about 0.8 percentage points. Results were robust to a battery of sensitivity checks that included the estimation of baseline specifications with different lag structures for the time-varying cyclicality measures; the removal of country and time fixed effects; the use of alternative dependent variables; the estimation by means of Weighted Least Squares to account for parameter uncertainty; and, finally, the estimation with two stage least squares to address potential endogeneity that used several political economy variables as exogenous instruments.

From a policymaking point of view, governments are advised to boost the counter-cyclical

Jou

nature of social public expenditure to maximize the natural operation of automatic stabilizers. Such effort also goes a long way to improve the risk sharing and insurance mechanism of households, employees, old-age, families, etc. against materialization of bad shocks. In face of the ongoing demographic transition and the fiscal consequences of population ageing, balance an increase spending on social protection and welfare with sustainability goals may prove a challenge. While devoting a larger share of the budget to this component is likely to put extra pressure on public

20

Journal Pre-proof

finances, our results also suggest that making these expenses more country-cyclical will help the

Declaration of Competing Interest

repro of

overall fiscal stabilization role provided by governments.

Jou

rna lP

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

21

Journal Pre-proof

References

Jou

rna lP

repro of

1. Acemoglu, D., S. Naidu, P. Restrepo, and J. A. Robinson (2013), “Democracy Does Cause Growth”, NBER Working Paper 20004. 2. Acemoglu, D., Johnson, S., Robinson, J. and Thaicharoen, Y. (2003), “Institutional causes, macroeconomic symptoms: volatility, crises and growth”, Journal of Monetary Economics, 50, 49– 123. 3. Aghion, P. and I. Marinescu (2008), “Cyclical Budgetary Policy and Economic Growth: What Do We Learn from OECD Panel Data?”, NBER Macroeconomics Annual, Volume 22 4. Aghion, P., G. Angeletos, A. Banerjee and K. Manova (2005), “Volatility and Growth: Credit Constraints and Productivity Enhancing Investment,” NBER Working Paper 11349. 5. Alesina, A., Campante, F.R., Tabellini G.R. (2008), “Why Is Fiscal Policy Often Procyclical?” Journal of the European Economic Association. 6(5), 1006-103 6. Barro, R. J. (1979), “On the determination of the public debt”, Journal of Political Economy, 87, 940–971. 7. Berman, N, Eymard, L, Aghion, P, Askenazy, P, and G. Cette (2007), “Credit Constraints and Cyclical R&D Investment: Evidence from French Firm-Level Panel Data”, mimeo Banque de France. 8. Borio, C. (2013), “On time, stocks and flows: understanding the global macroeconomic challenges”, National Institute Economic Review, August. Sligthly revised version of the lecture at the Munich Seminar series, CESIfo-Group and Süddeutsche Zeitung, 15 October, 2012, which is also available in BIS Speeches. 9. Calderón, C. and K. Schmidt-Hebbel. (2008), “Business Cycles and Fiscal Policies: The Role of Institutions and Financial Markets”, Central Bank of Chile Working Paper Nº 481, August. 10. Cogley, T. and J. Nason (1995), “Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research,” Journal of Economic Dynamics and Control, 19(1-2), 253- 278. 11. D’Addio, A. C. (2015), “The dynamics of social expenditure over the cycle: a comparison across OECD countries”, OECD Journal Economic Studies, 2015/1, 149-178 12. Darby, J. and Melitz, J. (2008), “Social spending and automatic stabilizers” Journal of International Economics, 55(1), 3-28. 13. Debrun, X. and R. Kapoor (2011), “Fiscal Policy and Macroeconomic Stability: New Evidence and Policy Implications”, Nordic Economic Policy Review, 1(1), 35-70. 14. Egert, B. (2012), “Fiscal Policy Reaction to the Cycle in the OECD: Pro-or Counter-cyclical”, CEsifo working papers, 3777. 15. Fatás, A. and I. Mihov (2001), “Government size and automatic stabilizers: international and intranational evidence”, Journal of International Economics, 55(1), 3-28. 16. Fatas, A. and I. Mihov (2012), “Fiscal policy as a stabilization tool”, B.E. Journal of Macroeconomics, 12(3), 1-68. 17. Fatas, A. and I. Mihov (2013), “Policy Volatility, Institutions, and Economic Growth”, Review of Economics and Statistics, 95: 362-376., 18. Furceri, D. (2010), “Stabilization effects of social spending: Empirical evidence from a panel of OECD countries”, North American Journal of Economics and Finance, 21(1), 34-48. 19. Furceri, D. and A., Zdzienicka (2012), “The effects of social spending on economic activity: empirical evidence form a panel of OECD countries”, Fiscal Studies, 33(1), 129-152. 20. Gavin, M., and R. Perotti (1997), “Fiscal Policy in Latin America.” NBER Macroeconomics Annual 1997, edited by Ben Bernanke and Julio Rotemberg. MIT Press

22

Journal Pre-proof

Jou

rna lP

repro of

21. Gavin, M., Hausmann, R., Perotti, R., and Talvi, E. (1996), “Managing Fiscal Policy in Latin America and the Caribbean: Volatility, Procyclicality, and Limited Creditworthiness”, mimeo, Office of the Chief Economist, InterAmerican Development Bank. 22. Granado, J., Gupta, S., Hajdenberg, A. (2013), “Is Social Spending Procyclical? Evidence for Developing Countries”, World Development, 42, 16-27. 23. Hallerberg, M. and R. Strauch (2002) On the Cyclicality of Public Finances in Europe. Empirica, 29, 183-207 24. Hamilton, J. (2018), “Why You Should Never Use the Hodrick-Prescott Filter,” Review of Economics and Statistics, 100(5), 831-843. 25. Harvey, A. and A. Jaeger (1993), “Detrending, Stylized Facts and the Business Cycle,” Journal of Applied Econometrics, 8(3), 231-47. 26. Kaminsky, G., C. Reinhart, and C. Végh, (2004), “When it Rains, it Pours: Procyclical Capital Flows and Macro Policies,” NBER Macroeconomics Annual 2004, edited by Mark Gertler and Kenneth Rogoff, 19: 54-61. Cambridge, MA: M.I.T. Press. 27. Kumhof, M., and D. Laxton, (2009), “Simple, Implementable Fiscal Policy Rules”, IMF Working Paper 09/76. 28. Lane, P. R. (1998), “On the Cyclicality of Irish Fiscal Policy,” Economic and Social Review, 29(1), 1–17. 29. Lane, P. R. (2003), “The Cyclical Behaviour of Fiscal Policy: Evidence from the OECD,” Journal of Public Economics, 87(12), 2661–2675. 30. Lee, C. and C. Chang (2006), “Social security expenditures and economic growth”, Journal of Economic Studies, 33(5), 386-404. 31. Ovaska, T. and J. Palardy (2014), “Business cycle volatility: does the European-style Safety Net help?”, Journal of Private Entreprise, 29(2), 57-81. 32. Prasad, N. and Gerecke, M. (2010), “Social Security Spending in Times of Crisis”, Global Social Policy, 10(2), 218-247. 33. Ramey, G. and V. A. Ramey (1995), “Cross-Country Evidence on the Link-Between Volatility and Growth”, American Economic Review, 85, 1138–51. 34. Rodrik, D. (1998), “Why Do More Open Economies Have Bigger Governments,” Journal of Political Economy 106, 997-1032. 35. Schlicht, E. (2003), “Estimating time-varying coefficients with the VC program”. Discussion Paper 2003-06. University of Munich 36. Schlicht, E. and Ludsteck, J., (2006), “Variance Estimation in a Random Coefficients Model”, IZA Discussion Papers 2031, Institute for the Study of Labor (IZA). 37. Schlicht, E., (1985), “Isolation and Aggregation in Economics”, Berlin-Heidelberg-New YorkTokyo: Springer-Verlag. 22. 38. Staehr, K. (2008), “Fiscal policies and business cycles in an enlarged Euro Area”, Economic Systems, 32, 46-49. 39. Talvi, E., and C. Vegh. (2005), “Tax Base Variability and Procyclical Fiscal Policy”, Journal of Development Economics, 78, 156-90. 40. Thornton, J. (2008), “Explaining Pro-Cyclical Fiscal Policy in African Countries,” Journal of African Economies, 17(3), 451–64. 41. Tornell, A., Lane, P. (1999), “The voracity effect”, American Economic Review, 89, 22- 46. 42. Tsebelis, G. (2002), “Veto Players”, Princeton, NJ: Princeton University Press. 43. van den Noord, P. (2000), “The Size and Role of Automatic Fiscal Stabilizers in the 1990s and Beyond”, OECD Working Paper 230. 44. Woo, J. (2009), “Why do more polarized countries run more pro-cyclical fiscal policy?”, Review of Economics and Statistics, 91(4), 850-870. 23

Journal Pre-proof

APPENDIX

List of Countries

repro of

US, UK, Austria, Belgium, Denmark, France, Germany, Italy, Netherlands, Norway, Sweden, Switzerland, Canada, Japan, Finland, Greece, Iceland, Ireland, Portugal, Spain, Australia, New Zealand, Israel, Hong Kong, Korea, Singapore. Figure A1. Time Varying social spending cyclicality by country

Cyclicality of Health Expenditures

1980

1990

2000

United States

1980

1990

2000

2010

1980

1990

2000

1.13436 -.865638

-.10908 -.10906 -.10904 -.10902 -.109

-.13996 -.13994 -.13992 -.1399

2010

-.15-.1 -.05 0

.9585 .959 .9595.96

Ireland

1980

1990

2000

New Zealand

.0191 .01915 .0192

-.1722 -.172 -.1718 -.1716 -.1714

Netherlands

Sweden

Switzerland

.5

Spain

2010

rna lP

-.8-.6-.4-.2 0

-.6-.5-.4-.3-.2

United Kingdom

Korea

Finland

Iceland

-.5

0

-.5 0 .5 1

Singapore

.325656 .325658 .32566 .325662 .325664

1 2 -1 0 -.15978 -.15977 -.15976 -.15975

Japan

.1356 .1358 .136 .1362

Portugal

1.494 1.4941 1.4942 1.4943

Norway

.45995 .46 .46005 .4601 .46015

Italy

.8789 .879 .8791 .8792 .8793

.25905 .2591 .25915 .2592

Israel

Denmark

Hong Kong SAR

.6232 .6233 .6234 .6235

Greece

Canada

.8093 .8094 .8095

0 1 2 3

Germany -2 0 2 4 6

.3234 .3235 .3236 .3237

France

Belgium

-.574 -.5738 -.5736 -.5734

Austria

0 1 2 3

Australia

1980

1990

2000

2010

1980

1990

2000

2010

2010

year

Cyclicality of Social Protection Expenditures

1990

2000

2010

-.9247 -.9246 -.9245 -.9244

1980

1990

2000

2010

1980

1990

2000

1980

year

1990

-1 -.5 0 .5 -.46925 -.4692 -.46915 -.4691

1 -1 0

-.4 -.3 -.2 -.1

New Zealand

-1 -.5 0 .5 2000

Ireland

Netherlands

2010

Sweden

1980

1990

2000

Switzerland

-1.4582 -1.458 -1.4578

Spain

2010

25

Iceland

Korea

United States

Finland

2

Hong Kong SAR

-.40805-.408-.40795

Singapore

Denmark -1-.5 0 .5 1

1.36382 -1.08056 -1.08054 -1.08052 -1.0805

0 -.5 -1

Jou 1980

-1.9603 -1.9602 -1.9601 -1.96

United Kingdom

Japan

Portugal

Canada

-1.3286 -1.3284 -1.3282 -1.328 -1.3278 -.636184

-.07648 -.07646 -.07644 -.07642

-5 0 -.0502 -.05 -.0498 -.0496

Italy

-.5 0 .5 1 1.5

-.8136 -.81355 -.8135

Norway

-.567 -.56695 -.5669 -.56685

.7492 .7494 .7496 .7498

Israel

Greece

-.62012 -.6201 -.62008 -.62006 -.62004

0 -.5

Germany

5 10

France

Belgium

-.4426 -.44255 -.4425 -.44245

Austria

.5

-.984272 -.984272 -.984271 -.41145 -.4114 -.41135

Australia

2010

1980

1990

2000

2010

1

2

0

1

2

3

-.5138 -.5137 -.5136 -.5135

1980 1990

Germany

Italy

Singapore

2000 2010

1980

1990 2000

2000 2010

Canada

Korea

United Kingdom

2010

.79207 .79208 .79209 .7921

1.097 1.0975 1.098 1.0985

1.18745 1.1875 1.18755 1.1876 1.18765

.73455 .7346 .73465 .7347

Norway

1980

Greece

1980 1990

1990

26 2000

year

2000

.9873 .9874 .9875 .9876

.801 .8011 .8012 .8013

Italy

2010

United States

2010 -2 0 2 4 6

United Kingdom

.8736 .8738 .874 .8742

1.3198 1.31982 1.31984 1.31986

Canada

Greece

Japan

Portugal

1980

Denmark

Hong Kong SAR

Netherlands

1980

1990

1990

2000

2000

.732 .73202 .73204 .73206 1.00473 1.00474 1.00475 1.00476 1.00477 1.1775 1.1776 1.1777 1.1778 1.1779 1.07659 1.0766 1.0766 1.07661 1.07661

1.2 1.4 1.6

.8058 .806 .8062 .8064 .8066

Belgium

Ireland 1.1562 1.1563 1.1564 1.1565

1990

Germany

.5 1 1.5 2 2.5

1980

.2 .4 .6

New Zealand

1980

year

Cyclicality of Education Expenditures

repro of

0 1 2 3

Israel

0

Switzerland .91401 .91402 .91403

.8 1 1.21.4

France

.72502 .72504 .72506 .72508 .7251

Sweden

-.38 -.375 -.37

33.5 44.5

11.1 1.2 1.3 1.4

Austria

-1 0 1 2 3

Belgium

1

2010

-2 -1 0

1.03148 1.03149 1.0315 1.03151

Ireland

.4219 .422 .4221 .4222 .4223

.6 .8 1 1.2

-1 0 1 2

Finland

-.0444-.0442-.044

2000

-.298 -.2978 -.2976 -.2974

1990

1.451.51.551.61.65

.02669 2.02669

Netherlands

.3926 .39262 .39264 .39266

.8 11.2 1.4 1.6

Australia

.03005 .0301 .03015 .0302 .03025

-.6648 -.6647 -.6646 -.6645 1980

rna lP

Jou -1 0

Journal Pre-proof

Cyclicality of Pension Expenditures

2010

Denmark

Iceland

Korea

Spain

United States

Finland

Norway

1980

1990

1990

2000

2000

2010

2010

France

Israel

Portugal

2010

Sweden

1980 1990 2000 -4.1 -4.05-4-3.95

Finland

Ireland

Netherlands

2010

New Zealand

1980 1990 2000

Israel

Switzerland

2010 -5

0

5

-1.3602 -1.36015 -1.3601 -1.36005

France

-4 -2 0 2

-9.858 -9.8575 -9.857 -9.8565 -9.856

-5 0 5 10

-.722 -.7215 -.721 -.7205 -.269608 -.269606 -.269604 -.269602 -1.3956 -1.3955 -1.3954 -1.3953 -1.3952

-2-1.5-1 -.5

-1.18586 -1.18584 -1.18582 -1.1858 -1.18578

Germany

1980

1990

27 2000

Italy

Norway

Portugal

United Kingdom

2010

1980

1990

2000

-1.3142 -1.314 -1.3138 -1.3136 -1.3134

Japan

-3.7288 -3.7286 -3.7284 -3.7282

-1 0 1 2 3

-1.5-1-.5 0

-1.0924 -1.0923 -1.0922 -1.9772 -1.97715 -1.9771 -1.97705

Belgium

-2.6779 -2.6778 -2.6777 -2.6776 -2.6775

6.77 6.78 6.79 6.8

-2.3565 -2.356

Austria

-1.03895 -1.0389 -1.03885 -1.0388

-1.8066 -1.8065 -1.8064 -1.8063 -1.8062 -1.1241 -1.12405 -1.124 -1.12395

Australia

year

Note: red line denotes the time-varying coefficients (TVC) estimates; black line is the average TVC.

repro of

rna lP

Jou -1.2947 -1.2946 -1.2945 -1.2944

Journal Pre-proof

Cyclicality of Welfare Expenditures Canada Denmark

Greece

Iceland

Korea

Spain

United States

1980

1990

2000

2010

2010

Journal Pre-proof

Table A1. Tests for autocorrelation and normality of the error terms of equation (2) Autocorrelation 0.197 (0.659)

Joint Normality test based on Skewness and Kurtosis (Chi square-test)

Note: p-values in parenthesis.

repro of

Wooldridge test for autocorrelation (F-test)

Normality

3.410 (0.524)

Table A2. Variables, definitions and sources Variables

Source

GDP per capita

Domestic credit to private sector refers to financial resources provided to the private sector by financial institutions (in percent of GDP) Real gross domestic product divided by population

Trade openness

Exports plus imports over GDP

Capital account openness Government expenditure to GDP Executive constraints Political constraints

KAOPEN is an index measuring a country's degree of capital account openness

Population

Total government expenditure to GDP ratio

This variable refers to the extent of institutionalized constraints on the decision-making powers of chief executives, whether individuals or collectivities. POLCON index takes into account the number of veto points faced by the executive power, as well as the distribution of political preferences across different branches of government. Total population

rna lP

Credit to GDP

Definition

World Bank, World Development Indicators World Bank, World Development Indicators IMF, International Financial Statistics Chinn-Ito Index of Financial Openness IMF, International Financial Statistics Polity IV Project Political Constraint Dataset, Henisz (2000) World Bank, World Development Indicators

Table A3. Summary Statistics Observations

Mean

Standard Deviation

Minimum

Maximum

Health spending (%GDP) Social protection spending (%GDP) Welfare spending (% GDP) Pensions spending (%GDP)

804 757 696 752

5.71 13.08 4.66 7.85

1.84 5.89 2.48 3.37

0.81 0.15 0.18 0.80

10.38 28.31 12.63 17.1

Education spending (% GDP) Trade openness Capital account openness Credit to GDP (log) GDP per capita (log) Real GDP growth Population (log) Government expenditure to GDP Executive constraints Political constraints

654 722 755 772 804 724 804 804 740 779

5.33 0.78 1.62 13.29 11.16 2.60 2.61 19.04 6.70 0.74

1.40 0.61 1.20 2.43 1.80 2.77 1.43 4.96 0.91 0.17

2.43 0.16 -0.185 5.10 9.01 -8.92 -1.46 7.63 3 0

12.42 4.38 2.45 20.95 16.91 14.02 5.74 38.15 7 0.89

Jou

Variables

28